{"ok":true,"nota":{"id":"91f652fc04c82764","slug":"identifican-que-genes-diferencian-a-las-celulas-de-cancer-de-mama-que--91f652","titulo":"Identifican qué genes diferencian a las células de cáncer de mama que invaden de las que no","resumen":"Reanalizaron datos de células de cáncer de mama triple negativo separadas según su capacidad de invadir tejidos. Combinaron distintas técnicas estadísticas y de inteligencia artificial para identificar qué actividad genética distingue a las células más invasivas.","fuente":"biorxiv","fuenteNombre":"bioRxiv · preprints","color":"#be2628","autor":"Huda, T., BISWAS, S.","url":"https://www.biorxiv.org/content/10.64898/2026.10.23.761315v1?rss=1","imagen":"/hoy/portada/91f652fc04c82764","tema":"salud","subtemas":["cáncer de mama","metástasis","single-cell","biomarcadores","inteligencia artificial"],"entidades":["bioRxiv","GSE329526","MDA-MB-231"],"formato":"dato","entretenimiento":false,"pubTs":"2026-10-27T00:00:00.000Z","indexTs":"2026-10-27T21:54:43.604Z","hash":"8991537be0e1a6af3c4f678537a0efb9c52820704ccd563d060ceddeb9e5961b","tsa":{"time":"Oct 27 21:55:38 2026 GMT","provider":"freetsa.org","sobre":"sello"},"stats":{"views":0,"likes":0,"clicks":0,"dwell":0},"tipo":"normal","registro":"hallazgo","porQueImporta":"La propagación del cáncer a otros órganos es lo que más complica el tratamiento, y el cáncer de mama triple negativo es uno de los más difíciles de tratar. Identificar genes asociados a la invasión abre la posibilidad de encontrar biomarcadores o nuevos blancos terapéuticos. El trabajo es un reanálisis de datos públicos sobre células en laboratorio, así que todavía falta comprobar cada hallazgo en pacientes. Ninguna de esas pruebas llega a la consulta de un médico en el corto plazo.","quienFirma":"bioRxiv (preprint; autores no identificados en el resumen disponible)","confianza":"preprint","consulta":null},"sello":{"hash":"8991537be0e1a6af3c4f678537a0efb9c52820704ccd563d060ceddeb9e5961b","sello":"69949e4a1ba0d2b5d55ced56f0425c41ebd5193e0840ffa92788d9ccbb101bfc","beacon":{"drand":{"cadena":"8990e7a9aaed2ffed73dbd7092123d6f289930540d7651336225dc172e51b2ce","ronda":6590236,"aleatorio":"37087642401cafb65cc63d07098b8c3fcee924c7b0ca4fe8ba6fe9e6dd0434fe","tiempo":"2026-10-27T21:55:00.000Z"},"nist":{"cadena":2,"pulso":2002052,"salida":"AC3E98FB168DE2701568D15C42ECF628500BDA14D48A7757D096B7D8822A43D570D8F6A1DC98C0BAED8287C9C6EBDE44CBEEB3D7A44D76309DDD579F9495C0E4","tiempo":"2026-10-27T21:54:00.000Z"},"noAntes":"2026-10-27T21:55:00.000Z"},"verificacion":{"drand":true,"nist":true},"tsa":{"time":"Oct 27 21:55:38 2026 GMT","provider":"freetsa.org","sobre":"sello"},"original":{"titulo":"Integrative Single-Cell Transcriptomics and Explainable AI for Disease Mechanism and Biomarker Discovery in Triple-Negative Breast Cancer","descripcion":"Invasion is an early step in the metastatic spread of triple-negative breast cancer (TNBC), yet cells within a single tumour or cell line differ in their capacity to invade, and it is unclear which transcriptional features distinguish cells that invade from those that do not. We re-analysed the public single-cell RNA-sequencing dataset GSE329526, in which ClonMapper-barcoded MDA-MB-231 cells were separated in a transwell assay into invasive (five samples, 6,289 cells) and non-invasive (five samples, 5,983 cells) fractions and compared with the parental population (one sample, 3,022 cells). An initial cell-level analysis combined Wilcoxon differential expression, an XGBoost classifier with a ","pubDate":"2026-10-27","url":"https://www.biorxiv.org/content/10.64898/2026.10.23.761315v1?rss=1"},"indexado":"2026-10-27T21:54:43.604Z"}}